Congestion control in the Internet has primarily been the responsibility of the end-to-end congestion control mechanisms of TCP (Transmission Control Protocol). However, with the rapid growth of the Internet and the strong requirements for quality of service (QoS) support, it has become clear that the Internet could not exclusively rely on the end hosts to perform the end-to-end congestion control. Mechanisms are needed in the intermediate network elements to complement the end hosts congestion control mechanisms. Recognizing this, the Internet Engineering Tasks Force (IETF) has advocated the deployment of active queue management (AQM) mechanisms at the intermediate network elements (routers, switches, etc.) as a means of congestion control.
To perform AQM, the network elements are equipped with the means to detect incipient congestion and to signal the traffic sources before congestion actually occurs. AQM mechanisms allow the network elements to send explicit/implicit feedback of congestion to the end hosts by marking/dropping packets. The end hosts in turn react to the packet marking/dropping by reducing their data transmission rates. The main goals of AQM are to reduce the average queue lengths in the network elements and thereby decrease the end-to-end delay experienced by end user traffic, and maximize the utilization of network resources by reducing the packet loss that occurs when queues overflow.
The traditional queue management mechanism is “drop tail” which is simple to implement and is widely deployed. With drop tail mechanism, packets are dropped only at buffer overflow at which point congestion signals are conveyed to the end hosts. This results in a significantly long time period between the instant when congestion occurs and the instant when the end hosts reduce their data transmission rates. During this time period, more packets may be sent by the end hosts which could be eventually dropped. Drop tail can lead to problematic global synchronization of the traffic sources (where the sources ramp their traffic rates and backoff at the same time) and periods of low link utilization.
A number of AQM schemes have been proposed which use either queue size information for congestion control (so-called queue-based schemes) or traffic rate information (so-called rate-based schemes). Queue-based AQM schemes adapt their marking/dropping probabilities in a manner that depends on the queue size at the link. With small buffers they tend to perform poorly. For example, studies have shown that a technique such as RED (Random Early Detection) described by S. Floyd and V. Jacobson in “Random Early Detection Gateway for Congestion Avoidance,” IEEE/ACM Trans. Networking, Vol. 1, No. 4, August 1993, pp. 397-413 performs well only when the queuing capacity is greater than the bandwidth-delay product. Quite naturally the dependence of a queue-based scheme on queue size information for control will require that there be sufficient buffering for effective process tracking and control.
Very small buffers tend to complicate the control problem in this case.
A number of recent studies such as C. V. Hollot, V. Misra, D. Townsley, and W. B. Gong, “A Control Theoretic Analysis of RED,” Proc. IEEE INFOCOM 2001, 2001, pp. 1510-1519, C. V. Hollot, V. Misra, D. Townsley, and W. B. Gong, “On Designing Improved Controllers for AQM Routers Supporting TCP Flows,” Proc. IEEE INFOCOM 2001, 2001, pp. 1726-1734, Y. Chait, C. V. Hollot, and V. Misra, “Fixed and Adaptive Model-Based Controllers for Active Queue Management,” Proc. American Control Conf., Arlington, Va., Jun. 25-27, 2001, pp. 2981-2986, and V. Misra, W. B. Gong, and D. Townsley, “Fluid-based Analysis of a Network of AQM Routers Supporting TCP Flows with an Application to RED,” Proc. ACM SIGCOMM 2000, 2000, pp. 151-160, have described queue-based AQM schemes which explicitly rely on dynamic modeling and feedback control principles. Central to these studies is the recognition that AQM schemes are essentially feedback control systems and that the principles of control theory can provide critical insight and guidance into the analysis and design of such schemes. The fluid flow analytical model of TCP described in “Fluid-based Analysis of a Network of AQM Routers Supporting TCP Flows with an Application to RED” by Misra et al. particularity expresses TCP dynamics in a form that allows control theoretic approaches to be applied for AQM design and analysis.
In view of the foregoing, it would be desirable to provide a technique for network queue management which overcomes the above-described inadequacies and shortcomings.